@article{mason:707,
	author = {Jonathan Mason and Paul S. Linsay and J. J. Collins and Leon Glass},
	collaboration = {},
	title = {Evolving complex dynamics in electronic models of genetic networks},
	publisher = {AIP},
	year = {2004},
	journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
	volume = {14},
	number = {3},
	pages = {707-715},
	keywords = {genetics; chaos; nonlinear dynamical systems; differential equations; CMOS logic circuits; RC circuits; limit cycles; evolution (biological)},
	url = {http://link.aip.org/link/?CHA/14/707/1},
	doi = {10.1063/1.1786683}
}

@article{francoishakim,
	author = {Francois, Paul and Hakim, Vincent}, 
	title = {Design of genetic networks with specified functions by evolution in silico}, 
	volume = {101}, 
	number = {2}, 
	pages = {580-585}, 
	year = {2004}, 
	doi = {10.1073/pnas.0304532101}, 
	abstract ={Recent studies have provided insights into the modular structure of genetic regulatory networks and emphasized the interest of quantitative functional descriptions. Here, to provide a priori knowledge of the structure of functional modules, we describe an evolutionary procedure in silico that creates small gene networks performing basic tasks. We used it to create networks functioning as bistable switches or oscillators. The obtained circuits provide a variety of functional designs, demonstrate the crucial role of posttranscriptional interactions, and highlight design principles also found in known biological networks. The procedure should prove helpful as a way to understand and create small functional modules with diverse functions as well as to analyze large networks.}, 
	URL = {http://www.pnas.org/content/101/2/580.abstract}, 
	eprint = {http://www.pnas.org/content/101/2/580.full.pdf+html}, 
	journal = {Proceedings of the National Academy of Sciences of the United States of America} 
}


@book{EdwardsPenney,
	abstract = {{This practical book reflects the new technological emphasis that permeates differential equations, including the wide availability of scientific computing environments like <I>Maple, Mathematica,</I></B></U> and MATLAB; it does not concentrate on traditional manual methods but rather on new computer-based methods that lead to a wider range of more realistic applications.  The book starts and ends with discussions of mathematical modeling of real-world phenomena, evident in figures, examples, problems, and applications throughout the book.  For mathematicians and those in the field of computer science.}},
	author = {Edwards, Henry C. and Penney, David E.},
	howpublished = {Hardcover},
	isbn = {0130673374},
	keywords = {reichlrecs},
	month = {jun},
	publisher = {{Prentice Hall}},
	title = {{Differential Equations: Computing and Modeling, Third Edition}},
	year = {2003}
}

@article{Lenski1999,
    abstract = {{Digital organisms are computer programs that self-replicate, mutate and adapt by natural selection1, 2, 3. They offer an opportunity to test generalizations about living systems that may extend beyond the organic life that biologists usually study. Here we have generated two classes of digital organism: simple programs selected solely for rapid replication, and complex programs selected to perform mathematical operations that accelerate replication through a set of defined 'metabolic' rewards. To examine the differences in their genetic architecture, we introduced millions of single and multiple mutations into each organism and measured the effects on the organism's fitness. The complex organisms are more robust than the simple ones with respect to the average effects of single mutations. Interactions among mutations are common and usually yield higher fitness than predicted from the component mutations assuming multiplicative effects; such interactions are especially important in the complex organisms. Frequent interactions among mutations have also been seen in bacteria, fungi and fruitflies4, 5, 6. Our findings support the view that interactions are a general feature of genetic systems7, 8, 9.}},
    author = {Lenski, R. and Ofria, C. and Collier, T. and Adami, C.},
    citeulike-article-id = {2606725},
    citeulike-linkout-0 = {http://www.nature.com/nature/journal/v400/n6745/full/400661a0.html},
    journal = {Nature},
    keywords = {bibtex-import, genetic, geneticmaterial},
    pages = {661--664},
    posted-at = {2008-03-28 13:41:14},
    priority = {2},
    title = {{Genome complexity, robustness and genetic interactions in digital organisms}},
    url = {http://www.nature.com/nature/journal/v400/n6745/full/400661a0.html},
    volume = {400},
    year = {1999}
}

@article{Adami2006,
    abstract = {{Digital genetics, or the genetics of digital organisms, is a new field of research that has become possible as a result of the remarkable power of evolution experiments that use computers. Self-replicating strands of computer code that inhabit specially prepared computers can mutate, evolve and adapt to their environment. Digital organisms make it easy to conduct repeatable, controlled experiments, which have a perfect genetic 'fossil record'. This allows researchers to address fundamental questions about the genetic basis of the evolution of complexity, genome organization, robustness and evolvability, and to test the consequences of mutations, including their interaction and recombination, on the fate of populations and lineages.}},
    author = {Adami, Christoph},

    day = {01},
    doi = {10.1038/nrg1771},
    issn = {1471-0056},
    journal = {Nature Reviews Genetics},
    keywords = {digital, evolution, review},
    month = feb,
    number = {2},
    pages = {109--118},
    pmid = {16418746},
    posted-at = {2006-01-19 19:36:40},
    priority = {2},
    publisher = {Nature Publishing Group},
    title = {{Digital genetics: unravelling the genetic basis of evolution}},
    url = {http://dx.doi.org/10.1038/nrg1771},
    volume = {7},
    year = {2006}
}

@article{Neyfakh2006,
	title = {A system for studying evolution of life-like virtual organisms},
	author = {Neyfakh Alex and Baranova Natalya and Mizrokhi Lev},
	year = {2006},
	abstract = {Abstract Background Fitness landscapes, the dependences of fitness on the genotype, are of critical importance for the evolution of living beings. Unfortunately, fitness landscapes that are relevant to the evolution of complex biological functions are very poorly known. As a result, the existing theory of evolution is mostly based on postulated fitness landscapes, which diminishes its usefulness. Attempts to deduce fitness landscapes from models of actual biological processes led, so far, to only limited success. Results We present a model system for studying the evolution of biological function, which makes it possible to attribute fitness to genotypes in a natural way. The system mimics a very simple cell and takes into account the basic properties of gene regulation and enzyme kinetics. A virtual cell contains only two small molecules, an organic nutrient A and an energy carrier X, and proteins of five types &#8211; two transcription factors, two enzymes, and a membrane transporter. The metabolism of the cell consists of importing A from the environment and utilizing it in order to produce X and an unspecified end product. The genome may carry an arbitrary number of genes, each one encoding a protein of one of the five types. Both major mutations that affect whole genes and minor mutations that affect individual characteristics of genes are possible. Fitness is determined by the ability of the cell to maintain homeostasis when its environment changes. The system has been implemented as a computer program, and several numerical experiments have been performed on it. Evolution of the virtual cells usually involves a rapid initial increase of fitness, which eventually slows down, until a fitness plateau is reached. The origin of a wide variety of genetic networks is routinely observed in independent experiments performed under the same conditions. These networks can have different, including very high, levels of complexity and often include large numbers of non-essential genes. Conclusion The described system displays a rich repertoire of biologically sensible behaviors and, thus, can be useful for investigating a number of unresolved issues in evolutionary biology, including evolution of complexity, modularity and redundancy, as well as for studying the general properties of genotype-to-fitness maps. Reviewers This article was reviewed by Drs. Eugene Koonin, Shamil Sunyaev and Arcady Mushegian.},
	publisher = {BioMed Central},
	url = {http://www.biology-direct.com/content/1/1/23},
	institution = {DOAJ-Articles [http://www.doaj.org/oai.article] (Sweden)}
}

@article{Bergman2003,
   author = {{Bergman}, A. and {Siegal}, M.~L.},
    title = "{Evolutionary capacitance as a general feature of complex gene networks}",
  journal = {\nat},
     year = {2003},
    month = {jul},
   volume = {424},
    pages = {549-552},
   adsurl = {http://adsabs.harvard.edu/abs/2003Natur.424..549B},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@book{Wagner2007,
    abstract = {{<p>All living things are remarkably complex, yet their DNA is unstable, undergoing countless random mutations over generations. Despite this instability, most animals do not grow two heads or die, plants continue to thrive, and bacteria continue to divide. <i>Robustness and Evolvability in Living Systems</i> tackles this perplexing paradox. The book explores why genetic changes do not cause organisms to fail catastrophically and how evolution shapes organisms' robustness. Andreas Wagner looks at this problem from the ground up, starting with the alphabet of DNA, the genetic code, RNA, and protein molecules, moving on to genetic networks and embryonic development, and working his way up to whole organisms. He then develops an evolutionary explanation for robustness.</p><p> Wagner shows how evolution by natural selection preferentially finds and favors robust solutions to the problems organisms face in surviving and reproducing. Such robustness, he argues, also enhances the potential for future evolutionary innovation. Wagner also argues that robustness has less to do with organisms having plenty of spare parts (the redundancy theory that has been popular) and more to do with the reality that mutations can change organisms in ways that do not substantively affect their fitness.</p><p> Unparalleled in its field, this book offers the most detailed analysis available of all facets of robustness within organisms. It will appeal not only to biologists but also to engineers interested in the design of robust systems and to social scientists concerned with robustness in human communities and populations.</p>}},
    author = {Wagner, Andreas},
    day = {02},
    edition = {1},
    howpublished = {Paperback},
    isbn = {0691134049},
    keywords = {adaptation, evolution, robustness},
    month = jul,
    posted-at = {2007-07-16 02:54:59},
    priority = {0},
    publisher = {Princeton University Press},
    title = {{Robustness and Evolvability in Living Systems: (Princeton Studies in Complexity)}},
    url = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/0691134049},
    year = {2007}
}

@Article{Agapakis2009,
author ="Agapakis, Christina M. and Silver, Pamela A.",
title  ="Synthetic biology: exploring and exploiting genetic modularity through the design of novel biological networks",
journal  ="Mol. BioSyst.",
year  ="2009",
volume  ="5",
issue  ="7",
pages  ="704-713",
publisher  ="The Royal Society of Chemistry",
doi  ="10.1039/B901484E",
url  ="http://dx.doi.org/10.1039/B901484E",
abstract  ="Synthetic biology has been used to describe many biological endeavors over the past thirty years-from designing enzymes and in vitro systems{,} to manipulating existing metabolisms and gene expression{,} to creating entirely synthetic replicating life forms. What separates the current incarnation of synthetic biology from the recombinant DNA technology or metabolic engineering of the past is an emphasis on principles from engineering such as modularity{,} standardization{,} and rigorously predictive models. As such{,} synthetic biology represents a new paradigm for learning about and using biological molecules and data{,} with applications in basic science{,} biotechnology{,} and medicine. This review covers the canonical examples as well as some recent advances in synthetic biology in terms of what we know and what we can learn about the networks underlying biology{,} and how this endeavor may shape our understanding of living systems."
}

@article{Buckler2009,
	location = {http://www.scientificcommons.org/46605979},
	title = {Protein sequestration generates a flexible ultrasensitive response in a genetic network},
	author = {Buchler, Nicolas E and Cross, Frederick R},
	keywords = {Report},
	year = {2009},
	abstract = {Ultrasensitive responses are crucial for cellular regulation. Protein sequestration, where an active protein is bound in an inactive complex by an inhibitor, can potentially generate ultrasensitivity. Here, in a synthetic genetic circuit in budding yeast, we show that sequestration of a basic leucine zipper transcription factor by a dominant-negative inhibitor converts a graded transcriptional response into a sharply ultrasensitive response, with apparent Hill coefficients up to 12. A simple quantitative model for this genetic network shows that both the threshold and the degree of ultrasensitivity depend upon the abundance of the inhibitor, exactly as we observed experimentally. The abundance of the inhibitor can be altered by simple mutation; thus, ultrasensitive responses mediated by protein sequestration are easily tuneable. Gene duplication of regulatory homodimers and loss-of-function mutations can create dominant negatives that sequester and inactivate the original regulator. The generation of flexible ultrasensitive responses is an unappreciated adaptive advantage that could explain the frequent evolutionary emergence of dominant negatives.},
	publisher = {Nature Publishing Group},
	url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2694680},
	institution = {PubMed Central (PMC3 - NLM DTD) http://www.pubmedcentral.nih.gov/oai/oai.cgi (United States)},
}

@book{edelstein1988,
  title={Mathematical models in biology},
  author={Edelstein-Keshet, L.},
  isbn={9780394355078},
  lccn={87020534},
  series={Random House/Birkh{\\"a}user mathematics series},
  url={http://books.google.com/books?id=Q8dwQgAACAAJ},
  year={1988},
  publisher={Random House}
}




